<div id="principal">
<p><strong>Carlos M. Carvalho</strong> - <a href="http://www2.mccombs.utexas.edu/faculty/carlos.carvalho/">http://www2.mccombs.utexas.edu/faculty/carlos.carvalho/ </a> </p>
<p>University of Texas, Austin, USA</p>
</font>




<ul style="text-align: justify;">

<li><font face="Arial" size="2" color="#000000">
<strong>T&iacute;tulo:</strong>
</font>
</li>
<p>Decoupled Shrinkage and Selection in Linear Models</p>

<li><font face="Arial" size="2" color="#000000">
<strong>Resumo:</strong>
</font>
</li>
<p id="resumo">We propose a new variable selection approach from a fully Bayesian decision theory viewpoint.  By drawing an explicit distinction between actions and inferences our method is able to effectively manage the the trade-off associated with the competing goals of predictive generalization and interpretability.  By decoupling posterior learning from model reporting, our approach creates a flexible framework where "sparse solutions'' can be obtained even when using continuous shrinkage priors.</p>
</div>
